Is your feature request related to a problem? Please describe. I tried running 'pysr' on a 1,000 row array with 4 integer input variables and one integer output variable - a Goedel Number.

From Mathematica:

GoedelNumber[l_List] := Times @@ MapIndexed[Prime[First[#2]]^#1 &, l]

E.g.

Data file:
# 7	1	5	8	6917761200000

julia> 2^7*3^1*5^5*7^8
6917761200000

The model returned:

Complexity  Loss       Score     Equation
1           Inf       NaN       0.22984365

I am just learning 'pysr' and maybe it's just 'user error'. However, Inf and Nan suggest that Goedel numbers may exceed Float64.

Describe the solution you'd like Not sure what happened, because the largest Goedel number in the input is: 1.6679880978201e+23

Additional context I didn't see any parameters to set 'verbose' mode or 'debugging' information.

GoedelTableFourParameters.txt

Hi Miles,

I've been installing PySR in parallel to Julia under win10. It runs... till the moment it crashes with the following message:

File "C:\Users\Matthieu\anaconda3\lib\site-packages\pysr\sr.py", line 774, in get_hof raise RuntimeError("Couldn't find equation file! The equation search likely exited before a single iteration completed.")

RuntimeError: Couldn't find equation file! The equation search likely exited before a single iteration completed.

In the last case, I've been to 38% of progress.

I have to say that, sometime (not often), the process gets completed.

What is the reason for this?

Also... is there a forum or I posted at the right place?

I thank you for your help.

Regards

Magaud

Describe the bug

JuliaError: Exception 'Unsatisfiable requirements detected for package DynamicExpressions [a40a106e]:
 DynamicExpressions [a40a106e] log:
 ├─DynamicExpressions [a40a106e] has no known versions!
 └─restricted to versions 0.4 by SymbolicRegression [8254be44] — no versions left
   └─SymbolicRegression [8254be44] log:
     ├─possible versions are: 0.14.4 or uninstalled
     └─SymbolicRegression [8254be44] is fixed to version 0.14.4' occurred while calling julia code:
Pkg.add([sr_spec, clustermanagers_spec], io=stderr)

Version (please include the following information): MacOS Ventura 13.0.1 (22A400)

• Julia version [Run julia --version in the terminal]

• julia --version julia version 1.8.3

• Python version [Run python --version in the terminal]

• Python 3.8.13

• Did you install with pip or conda?

• pip

$ conda list pysr
# packages in environment at /Users/davidlaxer/anaconda3/envs/ai:
#
# Name                    Version                   Build  Channel
pysr                      0.11.11                  pypi_0    pypi

% pip show pysr
Name: pysr
Version: 0.11.11
Summary: Simple and efficient symbolic regression
Home-page: https://github.com/MilesCranmer/pysr
Author: Miles Cranmer
Author-email: [email protected]
License: 
Location: /Users/davidlaxer/anaconda3/envs/ai/lib/python3.8/site-packages
Requires: julia, numpy, pandas, scikit-learn, sympy
Required-by: 

• PySR version [Run python -c 'import pysr; print(pysr.__version__)']
• 0.9.1
• Does the bug still appear with the latest version of PySR?

Configuration

• What are your PySR settings?
• What dataset are you running on?
• If possible, please share a minimal code example that produces the error.

Error message Add the error message here, or whatever other information would be useful for debugging.

If the error is "Couldn't find equation file...", this error indicates something went wrong with the backend. Please scroll up and copy the output of Julia, rather than the output of python.

Additional context Add any other context about the problem here.

Julia Version 1.8.3
Commit 0434deb161e (2022-11-14 20:14 UTC)
Platform Info:
  OS: macOS (x86_64-apple-darwin21.4.0)
  uname: Darwin 22.1.0 Darwin Kernel Version 22.1.0: Sun Oct  9 20:14:54 PDT 2022; root:xnu-8792.41.9~2/RELEASE_X86_64 x86_64 i386
  CPU: Intel(R) Core(TM) i7-10700K CPU @ 3.80GHz: 
                 speed         user         nice          sys         idle          irq
       #1-16  3800 MHz    7543546 s          0 s    3955434 s   72076495 s          0 s
  Memory: 128.0 GB (32470.4921875 MB free)
  Uptime: 951050.0 sec
  Load Avg:  8.20068359375  5.13525390625  4.3212890625
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-13.0.1 (ORCJIT, skylake)
  Threads: 1 on 16 virtual cores
Environment:
  JULIA_DEPOT_PATH_BACKUP = 
  JULIA_PROJECT_BACKUP = 
  JULIA_LOAD_PATH_BACKUP = 
  JULIA_DEPOT_PATH = /Users/davidlaxer/anaconda3/envs/ai/share/julia:
  JULIA_SSL_CA_ROOTS_PATH_BACKUP = 
  JULIA_SSL_CA_ROOTS_PATH = 
  JULIA_PROJECT = @pysr-0.11.11
  TERM = xterm-color
  PATH = /Users/davidlaxer/.opam/_coq-platform_.2021.02.1/bin:/Users/davidlaxer/.juliaup/bin:/Users/davidlaxer/.cabal/bin:/Users/davidlaxer/.ghcup/bin:/Users/davidlaxer/anaconda3/envs/ai/bin:/Users/davidlaxer/anaconda3/condabin:/opt/local/bin:/opt/local/sbin:/usr/local/bin:/System/Cryptexes/App/usr/bin:/usr/bin:/bin:/usr/sbin:/sbin:/Library/Apple/usr/bin:/Users/davidlaxer/.cargo/bin:/Users/jetbrains/.local/bin
  XPC_FLAGS = 0x0
  HOME = /Users/davidlaxer
  JAVA_HOME = :-
  JAVA_LD_LIBRARY_PATH = :-
  CAML_LD_LIBRARY_PATH = /Users/davidlaxer/.opam/_coq-platform_.2021.02.1/lib/stublibs:/Users/davidlaxer/.opam/_coq-platform_.2021.02.1/lib/ocaml/stublibs:/Users/davidlaxer/.opam/_coq-platform_.2021.02.1/lib/ocaml
  OCAML_TOPLEVEL_PATH = /Users/davidlaxer/.opam/_coq-platform_.2021.02.1/lib/toplevel
  PKG_CONFIG_PATH = /Users/davidlaxer/.opam/_coq-platform_.2021.02.1/lib/pkgconfig:
  CONDA_BACKUP_FFLAGS = -march=nocona -mtune=core2 -ftree-vectorize -fPIC -fstack-protector -O2 -pipe
  CONDA_BACKUP_FORTRANFLAGS = -march=nocona -mtune=core2 -ftree-vectorize -fPIC -fstack-protector -O2 -pipe
  CONDA_BACKUP_DEBUG_FFLAGS = -march=nocona -mtune=core2 -ftree-vectorize -fPIC -fstack-protector -O2 -pipe
  CONDA_BACKUP_DEBUG_FORTRANFLAGS = -march=nocona -mtune=core2 -ftree-vectorize -fPIC -fstack-protector -O2 -pipe
[ Info: Julia version info
[ Info: Julia executable: /Users/davidlaxer/anaconda3/envs/ai/share/julia/juliaup/julia-1.8.3+0.x64/bin/julia
[ Info: Trying to import PyCall...
┌ Info: PyCall is already installed and compatible with Python executable.
│ 
│ PyCall:
│     python: /Users/davidlaxer/anaconda3/envs/ai/bin/python
│     libpython: /Users/davidlaxer/anaconda3/envs/ai/lib/libpython3.8.dylib
│ Python:
│     python: /Users/davidlaxer/anaconda3/envs/ai/bin/python
└     libpython: 
   Resolving package versions...
---------------------------------------------------------------------------
JuliaError                                Traceback (most recent call last)
Input In [5], in <cell line: 4>()
      1 get_ipython().system('export JULIA_SSL_CA_ROOTS_PATH=""')
      2 import pysr
----> 4 pysr.install()

File ~/anaconda3/envs/ai/lib/python3.8/site-packages/pysr/julia_helpers.py:87, in install(julia_project, quiet)
     83 io_arg = _get_io_arg(quiet)
     85 if is_shared:
     86     # Install SymbolicRegression.jl:
---> 87     _add_sr_to_julia_project(Main, io_arg)
     89 Main.eval("using Pkg")
     90 Main.eval(f"Pkg.instantiate({io_arg})")

File ~/anaconda3/envs/ai/lib/python3.8/site-packages/pysr/julia_helpers.py:240, in _add_sr_to_julia_project(Main, io_arg)
    230 Main.sr_spec = Main.PackageSpec(
    231     name="SymbolicRegression",
    232     url="https://github.com/MilesCranmer/SymbolicRegression.jl",
    233     rev="v" + __symbolic_regression_jl_version__,
    234 )
    235 Main.clustermanagers_spec = Main.PackageSpec(
    236     name="ClusterManagers",
    237     url="https://github.com/JuliaParallel/ClusterManagers.jl",
    238     rev="14e7302f068794099344d5d93f71979aaf4fbeb3",
    239 )
--> 240 Main.eval(f"Pkg.add([sr_spec, clustermanagers_spec], {io_arg})")

File ~/anaconda3/envs/ai/lib/python3.8/site-packages/julia/core.py:627, in Julia.eval(self, src)
    625 if src is None:
    626     return None
--> 627 ans = self._call(src)
    628 if not ans:
    629     return None

File ~/anaconda3/envs/ai/lib/python3.8/site-packages/julia/core.py:555, in Julia._call(self, src)
    553 # logger.debug("_call(%s)", src)
    554 ans = self.api.jl_eval_string(src.encode('utf-8'))
--> 555 self.check_exception(src)
    557 return ans

File ~/anaconda3/envs/ai/lib/python3.8/site-packages/julia/core.py:609, in Julia.check_exception(self, src)
    607 else:
    608     exception = sprint(showerror, self._as_pyobj(res))
--> 609 raise JuliaError(u'Exception \'{}\' occurred while calling julia code:\n{}'
    610                  .format(exception, src))

JuliaError: Exception 'Unsatisfiable requirements detected for package DynamicExpressions [a40a106e]:
 DynamicExpressions [a40a106e] log:
 ├─DynamicExpressions [a40a106e] has no known versions!
 └─restricted to versions 0.4 by SymbolicRegression [8254be44] — no versions left
   └─SymbolicRegression [8254be44] log:
     ├─possible versions are: 0.14.4 or uninstalled
     └─SymbolicRegression [8254be44] is fixed to version 0.14.4' occurred while calling julia code:
Pkg.add([sr_spec, clustermanagers_spec], io=stderr)

 % julia
               _
   _       _ _(_)_     |  Documentation: https://docs.julialang.org
  (_)     | (_) (_)    |
   _ _   _| |_  __ _   |  Type "?" for help, "]?" for Pkg help.
  | | | | | | |/ _` |  |
  | | |_| | | | (_| |  |  Version 1.8.3 (2022-11-14)
 _/ |\__'_|_|_|\__'_|  |  Official https://julialang.org/ release
|__/                   |

julia> using Pkg

julia> Pkg.add("DynamicExpressions")
ERROR: The following package names could not be resolved:
 * DynamicExpressions (not found in project, manifest or registry)
Stacktrace:
  [1] pkgerror(msg::String)
    @ Pkg.Types ~/anaconda3/envs/ai/share/julia/juliaup/julia-1.8.3+0.x64/share/julia/stdlib/v1.8/Pkg/src/Types.jl:67
  [2] ensure_resolved(ctx::Pkg.Types.Context, manifest::Pkg.Types.Manifest, pkgs::Vector{Pkg.Types.PackageSpec}; registry::Bool)
    @ Pkg.Types ~/anaconda3/envs/ai/share/julia/juliaup/julia-1.8.3+0.x64/share/julia/stdlib/v1.8/Pkg/src/Types.jl:952
  [3] add(ctx::Pkg.Types.Context, pkgs::Vector{Pkg.Types.PackageSpec}; preserve::Pkg.Types.PreserveLevel, platform::Base.BinaryPlatforms.Platform, kwargs::Base.Pairs{Symbol, Base.TTY, Tuple{Symbol}, NamedTuple{(:io,), Tuple{Base.TTY}}})
    @ Pkg.API ~/anaconda3/envs/ai/share/julia/juliaup/julia-1.8.3+0.x64/share/julia/stdlib/v1.8/Pkg/src/API.jl:264
  [4] add(pkgs::Vector{Pkg.Types.PackageSpec}; io::Base.TTY, kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
    @ Pkg.API ~/anaconda3/envs/ai/share/julia/juliaup/julia-1.8.3+0.x64/share/julia/stdlib/v1.8/Pkg/src/API.jl:156
  [5] add(pkgs::Vector{Pkg.Types.PackageSpec})
    @ Pkg.API ~/anaconda3/envs/ai/share/julia/juliaup/julia-1.8.3+0.x64/share/julia/stdlib/v1.8/Pkg/src/API.jl:145
  [6] #add#27
    @ ~/anaconda3/envs/ai/share/julia/juliaup/julia-1.8.3+0.x64/share/julia/stdlib/v1.8/Pkg/src/API.jl:144 [inlined]
  [7] add
    @ ~/anaconda3/envs/ai/share/julia/juliaup/julia-1.8.3+0.x64/share/julia/stdlib/v1.8/Pkg/src/API.jl:144 [inlined]
  [8] #add#26
    @ ~/anaconda3/envs/ai/share/julia/juliaup/julia-1.8.3+0.x64/share/julia/stdlib/v1.8/Pkg/src/API.jl:143 [inlined]
  [9] add(pkg::String)
    @ Pkg.API ~/anaconda3/envs/ai/share/julia/juliaup/julia-1.8.3+0.x64/share/julia/stdlib/v1.8/Pkg/src/API.jl:143
 [10] top-level scope
    @ REPL[2]:1

julia> 

The code works properly on Google CoLab.

Re Issue #143

Compatibility with scikit-learn should be improved.

Noteable breaking changes for users: PySRRegressor.equations is now called PySRRegressor.equations_

Tests have been updated to allow compatibility with the refactored code but still assess the same functionality. All tests should pass.

Please let me know if there are any concerns or if you would like me to document/explain any of the changes in detail.

Edit: If you are seeing issues with the conda version, try updating PySR with conda update pysr. The new version fixes an issue related to automatic updating of Julia packages.

The conda-forge jobs which test conda install -c conda-forge pysr are currently breaking. This is even with repeat attempts: https://github.com/MilesCranmer/PySR/actions/workflows/CI_conda_forge.yml. The error:

ImportError: 
    Required dependencies are not installed or built.  Run the following code in the Python REPL:

I find this strange, since underlying feedstock has not changed in the meantime, and it seems like the julia feedstock hasn't been updated recently either.

FYI @mkitti @ngam. I will try to look into this a bit later today.

I have installed pysr-0.6.12.post1 and I have been try to run the example.py but after solve some previous closed bug reports a FileNotFoundError occurs. I'm using Windows 10 and Python 3.7 the version of Julia is 1.6.2. The error message is the following.

FileNotFoundError: [Errno 2] No such file or directory: 'hall_of_fame_2021-08-04_230410.180.csv.bkup'

Hello Miles! Thank you for open-sourcing this powerful tool! I am working on including PySR in my own research, and running into some performance bottlenecks.

I found regressing a simple equation (e.g. the quick-start example) takes roughly 2 minutes. Ideally, I am aiming to reduce that time to ~30 seconds. Would you give me some pointers on this? Meanwhile, I will try break down the challenge in several pieces:

1. Activating a new environment at each API call: I noticed that a new Julia (?) environment is created each time I call pysr() api (see terminal output below). Could we keep the environment up so we can skip this process for subsequent calls?

Running on julia -O3 /tmp/tmpe5qmgemh/runfile.jl
  Activating environment at `~/anaconda3/envs/rw/lib/python3.7/site-packages/Project.toml`
    Updating registry at `~/.julia/registries/General`
  No Changes to `~/anaconda3/envs/rw/lib/python3.7/site-packages/Project.toml`
  No Changes to `~/anaconda3/envs/rw/lib/python3.7/site-packages/Manifest.toml`
Activating environment on workers.
  Activating environment at `~/anaconda3/envs/rw/lib/python3.7/site-packages/Project.toml`
  Activating environment at `~/anaconda3/envs/rw/lib/python3.7/site-packages/Project.toml`
  Activating  Activating  environment at `~/anaconda3/envs/rw/lib/python3.7/site-packages/Project.toml`
environment at `~/anaconda3/envs/rw/lib/python3.7/site-packages/Project.toml`
  Activating  Activating environment at `~/anaconda3/envs/rw/lib/python3.7/site-packages/Project.toml` 
environment at `~/anaconda3/envs/rw/lib/python3.7/site-packages/Project.toml`
Importing installed module on workers...Finished!
Started!

2. If the above wouldn't work, then allowing y to be vector-valued (as mentioned in #35) would be a second-best option! Even better, if we could create a "batched" version of pysr(X, y) api pysr_batched(X, y), such that X and y are python lists, and we return the results in a list as well, so that we only generate one Julia script, and call os.system() once to keep the Julia environment up.

3. Multi-threading: I noticed that increasing procs from 4 to 8 resulted in slightly longer running time. I am running on a 8-core 16-tread CPU. Did I do something dumb?

4. I went into pysr/sr.py and added runtests=false flag in line 438 and 440. That saved ~20 seconds.

This generates a booktabs-style LaTeX table for a subset of equations. Here is an example:

import numpy as np
from pysr import PySRRegressor

X = 2 * np.random.randn(100, 5)
y = 2.5382 * np.cos(X[:, 3]) + X[:, 0] ** 2 - 0.5

model = PySRRegressor(
    niterations=80,
    binary_operators=["+", "*"],
    unary_operators=["cos"],
    model_selection="best",
    loss="loss(x, y) = (x - y)^2",  # Custom loss function (julia syntax)
    maxsize=11,
)

model.fit(X, y)

print(model.latex_table(precision=3, include_score=True))

The output of this is:

\begin{table}[h]
\begin{center}
\begin{tabular}{@{}lccc@{}}
\toprule
Equation & Complexity & Loss & Score \\
\midrule
$3.9$ & 1 & 38.9 & 0 \\
$x_{0}^{2}$ & 3 & 3.16 & 1.26 \\
$x_{0}^{2} - 0.257$ & 5 & 3.09 & 0.0105 \\
$x_{0}^{2} + \cos{\left(x_{3} \right)}$ & 6 & 1.26 & 0.898 \\
$x_{0}^{2} + 2.44 \cos{\left(x_{3} \right)}$ & 8 & 0.245 & 0.818 \\
$x_{0}^{2} + 2.54 \cos{\left(x_{3} \right)} - 0.5$ & 10 & 2.28e-13 & 13.9 \\
\bottomrule
\end{tabular}
\end{center}
\end{table}

which renders as:

Leaving include_score set to False will leave out the Score column. Precision can be adjusted to have more or less precise constants.

One can render only a subset of equations by using latex_table([1, 4]) which only includes the 1st and 4th equation in model.equations_.

Edit: it now renders the e-13 as \cdot 10^{-13}

• Sets JULIA_PROJECT before loading pyjulia so that PyCall.jl can be contained within the pysr environment
• Also use Pkg.add in a single step to add both SymbolicRegression.jl and ClusterManagers.jl to the environment at the same time

I likely advised against using the environment variable JULIA_PROJECT in the past. However, I think this may be necessary to avoid interference from other projects if installed within the same environment.

Hi Miles,

first of all, this is awesome. Thanks so much for making this.

A student I'm working with is trying to run PySR under Windows. Is that in principle supported?

PySR's dependencies don't seem to have any issues with Windows, but pysr.pysr throws a FileNotFoundError when accessing /tmp/.hyperparams_{rand_string}.hl'. Seems to be because of the different file system structure under Windows. If this is the only issue, how would you feel about using something like tempfile to generate temporary files in a more OS-independent way?

I am happy to try this and open a PR once it works.

Cheers, Johann

I don't know if this is a Windows issue or what (I work on a Linux partition, but I just wanted to play around with this - I haven't actually done serious work Windows for 7 years or so, so I'm at a loss), but after fitting one equation, it's always returning that equation. Even with different data, in a different notebook.

I've looked to see if I could find the julia file it creates - nope. And they're different files every time.

Any ideas?

I see this error when I try to use the ReLU operator:

PicklingError: Can't pickle relu: attribute lookup relu on __main__ failed

seems like it's implemented in a way that can't be pickled. Should be an easy fix.

Many of the Windows tests are now failing with various segmentation faults, which appear to be randomly triggered:

• Nightly action: https://github.com/MilesCranmer/PySR/actions/workflows/CI_large_nightly.yml
• PR action: https://github.com/MilesCranmer/PySR/pull/237

They seem to occur more frequently on older versions of Julia, and rarely on Julia 1.8.3. Regardless, a segfault anywhere is cause for concern and should be tracked down.

The errors include:

1. Early segmentation fault (Julia 1.6.7) at first run, segfault during noise test (Julia 1.6.7 and others), as well as segfaults during warm start test.

e.g., Windows:

 D:\a\_temp\221410f9-8bf7-4099-901d-eb9813d86c45.sh: line 1:  1098 Segmentation fault      python -m pysr.test main
Started!

signal (11): Segmentation fault
in expression starting at none:0
unknown function (ip: 0x7fd6a19bc215)
unknown function (ip: 0x7fd6a19947ff)
macro expansion at /home/runner/.julia/packages/PyCall/ygXW2/src/exception.jl:95 [inlined]
convert at /home/runner/.julia/packages/PyCall/ygXW2/src/conversions.jl:94
pyjlwrap_getattr at /home/runner/.julia/packages/PyCall/ygXW2/src/pytype.jl:378
unknown function (ip: 0x7fd68d30b1bd)
unknown function (ip: 0x7fd6a19babda)
unknown function (ip: 0x7fd6a198e9d4)
pyisinstance at /home/runner/.julia/packages/PyCall/ygXW2/src/PyCall.jl:170 [inlined]
pysequence_query at /home/runner/.julia/packages/PyCall/ygXW2/src/conversions.jl:752
pytype_query at /home/runner/.julia/packages/PyCall/ygXW2/src/conversions.jl:773
pytype_query at /home/runner/.julia/packages/PyCall/ygXW2/src/conversions.jl:806 [inlined]
convert at /home/runner/.julia/packages/PyCall/ygXW2/src/conversions.jl:831
julia_kwarg at /home/runner/.julia/packages/PyCall/ygXW2/src/callback.jl:19 [inlined]
#57 at ./none:0 [inlined]
iterate at ./generator.jl:47 [inlined]
collect_to! at ./array.jl:728
unknown function (ip: 0x7fd68d341d9a)
_jl_invoke at /buildworker/worker/package_linux64/build/src/gf.c:2237 [inlined]
jl_apply_generic at /buildworker/worker/package_linux64/build/src/gf.c:2419
collect_to! at ./array.jl:736
unknown function (ip: 0x7fd68d33e35a)
_jl_invoke at /buildworker/worker/package_linux64/build/src/gf.c:2237 [inlined]
jl_apply_generic at /buildworker/worker/package_linux64/build/src/gf.c:2419
collect_to! at ./array.jl:736
collect_to_with_first! at ./array.jl:706
unknown function (ip: 0x7fd68d33d775)
_jl_invoke at /buildworker/worker/package_linux64/build/src/gf.c:2237 [inlined]
jl_apply_generic at /buildworker/worker/package_linux64/build/src/gf.c:2419
collect at ./array.jl:687
unknown function (ip: 0x7fd68d33afb4)
_jl_invoke at /buildworker/worker/package_linux64/build/src/gf.c:2237 [inlined]
jl_apply_generic at /buildworker/worker/package_linux64/build/src/gf.c:2419
_pyjlwrap_call at /home/runner/.julia/packages/PyCall/ygXW2/src/callback.jl:31
unknown function (ip: 0x7fd68d3348d5)
_jl_invoke at /buildworker/worker/package_linux64/build/src/gf.c:2237 [inlined]
jl_apply_generic at /buildworker/worker/package_linux64/build/src/gf.c:2419
pyjlwrap_call at /home/runner/.julia/packages/PyCall/ygXW2/src/callback.jl:44
unknown function (ip: 0x7fd68d30aeee)
unknown function (ip: 0x7fd6a19980c7)
_PyObject_VectorcallTstate at /home/runner/work/_temp/SourceCode/./Include/cpython/abstract.h:116 [inlined]
_PyObject_VectorcallTstate at /home/runner/work/_temp/SourceCode/./Include/cpython/abstract.h:103 [inlined]
PyObject_Vectorcall at /home/runner/work/_temp/SourceCode/./Include/cpython/abstract.h:127 [inlined]
call_function at /home/runner/work/_temp/SourceCode/Python/ceval.c:5077 [inlined]
_PyEval_EvalFrameDefault at /home/runner/work/_temp/SourceCode/Python/ceval.c:3537
unknown function (ip: 0x7fd6a19ebbb7)
_PyFunction_Vectorcall at /home/runner/work/_temp/SourceCode/Objects/call.c:396
unknown function (ip: 0x7fd6a199a1e0)
unknown function (ip: 0x7fd6a19ed97b)
unknown function (ip: 0x7fd6a19ebbb7)
_PyFunction_Vectorcall at /home/runner/work/_temp/SourceCode/Objects/call.c:396
unknown function (ip: 0x7fd6a19ecdf6)
unknown function (ip: 0x7fd6a1998972)
unknown function (ip: 0x7fd6a199a1e0)
unknown function (ip: 0x7fd6a19ecb12)
unknown function (ip: 0x7fd6a1998972)
unknown function (ip: 0x7fd6a19ecdf6)
unknown function (ip: 0x7fd6a19ebbb7)
_PyFunction_Vectorcall at /home/runner/work/_temp/SourceCode/Objects/call.c:396
unknown function (ip: 0x7fd6a199a28d)
unknown function (ip: 0x7fd6a19ef9b1)
unknown function (ip: 0x7fd6a19ebbb7)
unknown function (ip: 0x7fd6a1997d4c)
unknown function (ip: 0x7fd6a1998f2b)
unknown function (ip: 0x7fd6a1a46421)
unknown function (ip: 0x7fd6a199802f)
_PyObject_VectorcallTstate at /home/runner/work/_temp/SourceCode/./Include/cpython/abstract.h:116 [inlined]
_PyObject_VectorcallTstate at /home/runner/work/_temp/SourceCode/./Include/cpython/abstract.h:103 [inlined]
PyObject_Vectorcall at /home/runner/work/_temp/SourceCode/./Include/cpython/abstract.h:127 [inlined]
call_function at /home/runner/work/_temp/SourceCode/Python/ceval.c:5077 [inlined]
_PyEval_EvalFrameDefault at /home/runner/work/_temp/SourceCode/Python/ceval.c:3520
unknown function (ip: 0x7fd6a19ebbb7)
_PyFunction_Vectorcall at /home/runner/work/_temp/SourceCode/Objects/call.c:396
unknown function (ip: 0x7fd6a199a28d)
unknown function (ip: 0x7fd6a19ef9b1)
unknown function (ip: 0x7fd6a19ebbb7)
unknown function (ip: 0x7fd6a1997d4c)
unknown function (ip: 0x7fd6a1998f2b)
unknown function (ip: 0x7fd6a1a46421)
unknown function (ip: 0x7fd6a199802f)
_PyObject_VectorcallTstate at /home/runner/work/_temp/SourceCode/./Include/cpython/abstract.h:116 [inlined]
_PyObject_VectorcallTstate at /home/runner/work/_temp/SourceCode/./Include/cpython/abstract.h:103 [inlined]
PyObject_Vectorcall at /home/runner/work/_temp/SourceCode/./Include/cpython/abstract.h:127 [inlined]
call_function at /home/runner/work/_temp/SourceCode/Python/ceval.c:5077 [inlined]
_PyEval_EvalFrameDefault at /home/runner/work/_temp/SourceCode/Python/ceval.c:3520
unknown function (ip: 0x7fd6a1998972)
unknown function (ip: 0x7fd6a19ecdf6)
unknown function (ip: 0x7fd6a1998972)
unknown function (ip: 0x7fd6a19ecb12)
unknown function (ip: 0x7fd6a19ebbb7)
_PyEval_EvalCodeWithName at /home/runner/work/_temp/SourceCode/Python/ceval.c:4361
unknown function (ip: 0x7fd6a19eb876)
PyEval_EvalCode at /home/runner/work/_temp/SourceCode/Python/ceval.c:828
unknown function (ip: 0x7fd6a1a6399f)
cfunction_vectorcall_FASTCALL at /home/runner/work/_temp/SourceCode/Objects/methodobject.c:430
unknown function (ip: 0x7fd6a19ecb12)
unknown function (ip: 0x7fd6a19ebbb7)
_PyFunction_Vectorcall at /home/runner/work/_temp/SourceCode/Objects/call.c:396
unknown function (ip: 0x7fd6a19ecb12)
unknown function (ip: 0x7fd6a19ebbb7)
_PyFunction_Vectorcall at /home/runner/work/_temp/SourceCode/Objects/call.c:396
unknown function (ip: 0x7fd6a1a7fdd6)
unknown function (ip: 0x7fd6a1a7faae)
Py_BytesMain at /home/runner/work/_temp/SourceCode/Modules/main.c:731
unknown function (ip: 0x7fd6a1642d8f)
__libc_start_main at /lib/x86_64-linux-gnu/libc.so.6 (unknown line)
_start at python (unknown line)
Allocations: 185387713 (Pool: 185351460; Big: 36253); GC: 470
/home/runner/work/_temp/bdd49862-48fd-4e82-bed8-685329606248.sh: line 1:  2324 Segmentation fault      (core dumped) python -m pysr.test main

2. Git errors: (Julia 1.8.2)

PyCall is installed and built successfully.
     Cloning git-repo `[https://github.com/MilesCranmer/SymbolicRegression.jl`](https://github.com/MilesCranmer/SymbolicRegression.jl%60)
Traceback (most recent call last):
  File "<string>", line 1, in <module>
  File "/Users/runner/work/PySR/PySR/pysr/julia_helpers.py", line 87, in install
    _add_sr_to_julia_project(Main, io_arg)
  File "/Users/runner/work/PySR/PySR/pysr/julia_helpers.py", line 240, in _add_sr_to_julia_project
    Main.eval(f"Pkg.add([sr_spec, clustermanagers_spec], {io_arg})")
  File "/Users/runner/hostedtoolcache/Python/3.9.14/x64/lib/python3.9/site-packages/julia/core.py", line 627, in eval
    ans = self._call(src)
  File "/Users/runner/hostedtoolcache/Python/3.9.14/x64/lib/python3.9/site-packages/julia/core.py", line 555, in _call
    self.check_exception(src)
  File "/Users/runner/hostedtoolcache/Python/3.9.14/x64/lib/python3.9/site-packages/julia/core.py", line 609, in check_exception
    raise JuliaError(u'Exception \'{}\' occurred while calling julia code:\n{}'
julia.core.JuliaError: Exception 'failed to clone from https://github.com/MilesCranmer/SymbolicRegression.jl, error: GitError(Code:ERROR, Class:Net, SecureTransport error: connection closed via error)' occurred while calling julia code:
Pkg.add([sr_spec, clustermanagers_spec], io=stderr)

3. Access errors during scikit-learn tests (these ones don't even fail the CI, which is a bit worrisome)

e.g.,

Failed check_fit2d_predict1d with:
    Traceback (most recent call last):
      File "D:\a\PySR\PySR\pysr\test\test.py", line 671, in test_scikit_learn_compatibility
        check(model)
      File "C:\hostedtoolcache\windows\Python\3.9.13\x64\lib\site-packages\sklearn\utils\_testing.py", line 188, in wrapper
        return fn(*args, **kwargs)
      File "C:\hostedtoolcache\windows\Python\3.9.13\x64\lib\site-packages\sklearn\utils\estimator_checks.py", line 1300, in check_fit2d_predict1d
        estimator.fit(X, y)
      File "D:\a\PySR\PySR\pysr\sr.py", line 1792, in fit
        self._run(X, y, mutated_params, weights=weights, seed=seed)
      File "D:\a\PySR\PySR\pysr\sr.py", line 1493, in _run
        Main = init_julia(self.julia_project, julia_kwargs=julia_kwargs)
      File "D:\a\PySR\PySR\pysr\julia_helpers.py", line 180, in init_julia
        Julia(**julia_kwargs)
      File "C:\hostedtoolcache\windows\Python\3.9.13\x64\lib\site-packages\julia\core.py", line 519, in __init__
        self._call("const PyCall = Base.require({0})".format(PYCALL_PKGID))
      File "C:\hostedtoolcache\windows\Python\3.9.13\x64\lib\site-packages\julia\core.py", line 554, in _call
        ans = self.api.jl_eval_string(src.encode('utf-8'))
    OSError: exception: access violation reading 0x000001BC1C501000

4. Torch errors.

One other curious thing is that this error is raised on some Windows tests (https://github.com/MilesCranmer/PySR/actions/runs/3664894286/jobs/6195713513). But, this should not take place...

Run python -m pysr.test torch
D:\a\PySR\PySR\pysr\julia_helpers.py:139: UserWarning: `torch` was loaded before the Julia instance started. This may cause a segfault when running `PySRRegressor.fit`. To avoid this, please run `pysr.julia_helpers.init_julia()` *before* importing `torch`. For updates, see https://github.com/pytorch/pytorch/issues/78829
  warnings.warn(
D:\a\_temp\8727c9f4-d0f6-4345-84e6-e774762771ab.sh: line 1:   258 Segmentation fault      python -m pysr.test torch
Started!

Time-to-first-search is very slow on statically-linked versions of Python (such as packaged with conda), as precompiled code cannot be used, so things are compiled from scratch. I think this adds some friction to the user experience, so this PR introduces a warning that recommends the user try pyenv if startup time is important.

When https://github.com/JuliaPy/pyjulia/issues/496 is solved, this warning is no longer needed.

See https://github.com/conda-forge/python-feedstock/issues/222 for the discussion on the conda page.

Right now you install SymbolicRegression.jl using python -c 'import pysr; pysr.install()'. However, this is a bit of spooky action at a distance, because you can't quite be sure which pysr is actually being called. Thus, it would be great if there was a CLI, similar to how testing is done with python -m pysr.test main. For example:

python -m pysr.install

If anybody wants to add this, I'd be more than happy to accept a PR!